The posterior probability density functions presented in this paper were sampled using the PyCBC Inference software. The PyCBC Inference toolkit uses the Bayesian inference methodology described in this paper; a more detailed description of the toolkit is presented in Ref. [1]}. The source code and documentation of PyCBC Inference is available as part of the PyCBC software package at http://pycbc.org. The results in this paper were generated with the PyCBC version 1.12.3 release. In the data release repository for this work [2]} we provide scripts and configuration files for replicating our analysis. The scripts document our command line calls to the pycbc_inference executable which performs the ensemble MCMC analyses. The command line call to pycbc_inference contains options for: the ensemble MCMC configuration, data conditioning, and locations of the configuration file and gravitational-wave detector data files. The configuration files included in the repository, and used as an input to pycbc_inference, specify the prior probability density functions used in the analyses, including sections for: initializing the distribution of Markov-chain positions in the ensemble MCMC, declaring transformations between the parameters that define the prior and the parameters the ensemble MCMC samples (eg. \((m_1, m_2) \rightarrow (\mathcal {M}, q)\) ), and defining additional constraints to the prior probability density function [1]}.
